| Paper ID | SPTM-13.2 |
| Paper Title |
ALIGNING SETS OF TEMPORAL SIGNALS WITH RIEMANNIAN GEOMETRY AND KOOPMAN OPERATOR |
| Authors |
Ohad Rahamim, Ronen Talmon, Technion - Israel Institute of Technology, Israel |
| Session | SPTM-13: Models, Methods and Algorithms 1 |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [SSP] Statistical Signal Processing |
| IEEE Xplore Open Preview |
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| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
In this paper, we consider the problem of aligning data sets of short temporal signals without any a-priori known correspondence. We present a method combining Koopman operator theory and the Riemannian geometry of symmetric positive-definite (SPD) matrices. First, by taking a Koopman operator theory standpoint, we build feature matrices of the signals using dynamic mode decomposition (DMD). Second, we align these features using parallel transport of SPD matrices, built from the DMD feature matrices. We showcase the performance of the proposed method on simulated observations of a mechanical system and on two real-world applications: sleep stage identification and pre-epileptic seizure prediction. |
